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Industry Insight: How Analytics has Affected the Global Sports Industry

We explore how data analytics has effected popular sports within the industry.

09/06/2015

In 2003 Michael Lewis published “Moneyball: The Art of Winning an Unfair Game” which has since been seen by many armchair enthusiasts as the modern day birth of data analysis in sport. The use of professional analytics to deliver performance however, started long before and can be seen in multiple examples across different sports. In 1986, ‘Stack’ the motorsport data logging specialist introduced the first stepper motor tachometer, while Otpa Sports formed only three years after the inaugural Premier League season in 1996.

Now, in almost all professional sports, data analytics is used across the whole spectrum from player recruitment, nutrition, rehabilitation from injury, both in training and competitive environments. It’s also used to quantify and monitor the workload of the players and by television companies to better aid spectator enjoyment. It’s also used by betting companies providing a kaleidoscope of statistics to their customers and recently data has also been used by UK cycling as rehabilitation from the sports much maligned drug past.

Recently my colleague Viki Dowthwaite explored how data analytics has been used in the recent Monaco Grand Prix. Data used by Formula One teams plays a massive part in both driver physiology and car performance. The data is used to almost limitless potential from engine and tire temperature management to cornering and breaking efficiencies. The purpose is to optimise all mechanical or non-human elements of the car for the driver.

If we take a moment to focus on Cricket, a sport that shares many similarities with data driven baseball, we can see that the ECB, as recently as 2012, appointed Opta as the official data providers to English cricket. A statistical approach to cricket now affects all areas of game management, where previously circumstances and tactics might have been left to the gut feeling of opposing captains. On field tactics are now based around statistical weakness of opposite players with both batting and bowling approaches tailored to areas that will have higher probability of positive outcomes for the offensive team. Cricket is becoming a game based on data science and statistics rather than human intuition.

In the paragraph above I briefly mentioned UK cycling adoption of data analysis and during the 2012 Olympics there was intense media scrutiny of what has now become known as the Secret Squirrel Club. We now know this as a technical research project hoping to push the GB team to the pinnacle of the sport. It was a huge investment with the sole purpose of establishing marginal gains and Dave Brailsford, the Head of UK Cycling was quoted as saying, "The whole principle came from the idea that if you broke down everything you could think of that goes into riding a bike, and then improved it by 1%, you will get a significant increase when you put them all together." Essentially, they used data from every aspect of the sport to develop measurable improvements in rider’s performances.

Two years ago rugby union opened up its use of analytics to the public in the 2013 Six Nation Tournament. It gave a fantastic insight into how the sport had been using the data it collected. One of the more interesting aspects is that calculations are made around how the actions and decisions of a player can contribute to a point scoring opportunity. Algorithms track player’s involvement and calculate scores based on game data such as passes, ball retention, territory, missed tackles, yards gained and handling errors. Often there were real differences in official man of the match awards and players topping these algorithm leagues, highlighting again that human intuition and gut feelings are often proved wrong by actual factual match data.

Data in football started with counting shots on and off target and progressed to possession and pass completion. The modern game of football in 2015 experiences real-time data delivered to backroom staff during play via mobile devices that cover individual heat maps of players, key passes and successful passes in the final third, distance travelled with detailed analysis and breakdown of intensity with which players travel that distance. During the Champions league final Juan Carlos Unzué, Barcelona Assistant Manager can be seen holding his iPad while organising substations and providing them with real-time information before entering the pitch. Football has invested heavily into the use of data the now determines pre-match preparation and post-game debriefs. It helps to identify transfer targets and what they are really worth to the future of the club, both on and off the field.

It’s now clear that every match-up in any professional sport will contain some form of sports analyst watching on. There have been headlines written proclaiming that "geeks" and spreadsheets are taking over the sports many people love, but the one theme that has be ever present in the paragraphs above is that data analysis and predictive analytics have improved the industry. The games we watch and the professional athletics have become far better than they would have been 10 years ago. Lionel Messi during the CL final last Saturday knew exactly how good Patrice Eva was when being taken on either the inside or outside and what his tackle success rates would be in these given situations.

The sports industry has become a business, and like global business, everybody is looking to gain a competitive advantage by marginal gains in performance. Thirty years ago it was new and exciting products that gave companies the competitive advantage. In a world that now has a globalised economy, the only way to truly achieve growth and sustain competiveness is to look internally and use data to improve efficiency, attract new customers while maximising both their loyalty and spend. It’s hardly surprising then that sport is using data in exactly the same way.

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